CN115795860A - Genetic algorithm-based high-power electromagnetic energy harvester structure optimization method - Google Patents
Genetic algorithm-based high-power electromagnetic energy harvester structure optimization method Download PDFInfo
- Publication number
- CN115795860A CN115795860A CN202211506528.7A CN202211506528A CN115795860A CN 115795860 A CN115795860 A CN 115795860A CN 202211506528 A CN202211506528 A CN 202211506528A CN 115795860 A CN115795860 A CN 115795860A
- Authority
- CN
- China
- Prior art keywords
- energy harvester
- population
- electromagnetic energy
- magnet
- parameter
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Images
Landscapes
- Control Of Vehicles With Linear Motors And Vehicles That Are Magnetically Levitated (AREA)
Abstract
The invention discloses a genetic algorithm-based structure optimization method for a high-power electromagnetic energy harvester, and belongs to the field of energy harvesters. The energy harvester consists of a fixed magnet, a suspension magnet, a coil, a shell and a load resistor, wherein the fixed magnet is arranged at the top end and the bottom end and consists of a large stator and a small stator. The fixed magnets and the suspension magnets are arranged in a mutually exclusive mode, and coils are wound on the upper section and the lower section of the shell of each fixed magnet. The energy harvester can generate larger power when the excitation frequency is close to the resonance frequency of the energy harvester. Therefore, the genetic algorithm is used for carrying out gene coding on parameters such as the outer diameter and height of the large stator and the small stator, the distance between the small stators and the suspension magnets, the distance between the coils, the number of turns and the thickness, and the like, the electro-magnetic-solid dynamic response simulation is developed, the constraint condition that the resonance frequency of the energy harvester falls within the excitation frequency band and the total height of the energy harvester is not more than a certain size is realized by the method of endowing the adaptability, and the structural scheme of the energy harvester for capturing higher power is obtained after multi-generation iteration.
Description
Technical Field
The invention relates to a method for optimizing a structure of a high-power electromagnetic energy harvester based on a genetic algorithm, and belongs to the field of energy harvesters.
Background
As technology develops, various ways of supplying battery power have emerged. For a wireless sensor under the condition that the working environment is severe and the battery cannot be replaced for a long time, the traditional battery energy supply mode cannot be met, and therefore a mode capable of stably supplying power for a long time is needed.
To this end, energy harvesting technology is an effective solution, and energy harvesters made according to its principles can harvest energy from the environment and convert it into the electrical energy required by the sensors. The mechanical vibration energy is a green renewable energy source which can be continuously collected in the environment, and has the advantage of being not influenced by factors such as weather and positions. The vibration energy collecting device can collect mechanical vibration energy in the environment, then converts the collected energy into electric energy, and can supply power to equipment such as a sensor and the like all weather.
Vibration energy harvesting systems utilize piezoelectric, electromagnetic or electrostatic techniques to convert the kinetic energy contained in these oscillations into useful electrical energy. Although the vibration energy is very abundant, the vibration energy harvester has a very low utilization of the vibration energy. One is that the self resonance frequency of the energy harvester is not matched with the vibration source frequency; secondly, the structure has a significant effect on the resonant frequency of the energy harvester. If the parameters are not properly selected, the power of the energy harvester can be limited because the excitation frequency matches the resonant frequency of the energy harvester, the energy harvester can produce significant power. But the energy harvester has a substantial reduction in power output in terms of off-resonance results. Magnetic springs are one of the commonly used techniques for nonlinear energy harvesting systems. The magnetic spring consists of two fixed magnets with a third magnet suspended between them. The three magnets are arranged in a mutually repulsive manner. The non-linear magnetic force generated between the floating magnet and the static magnet is the reason for the non-linearity of energy collection of the energy harvester.
How to carry out structure optimization on the high-power electromagnetic energy harvester can obtain an optimal parameter solution with high efficiency is a technical problem to be solved urgently at present.
Disclosure of Invention
The invention aims to provide a method for optimizing the structural parameters of an electromagnetic energy harvester under the constraint conditions of the structure and the performance of the electromagnetic energy harvester (for example, the resonance frequency is in a range of more than 150Hz and less than 170Hz, and the overall height is less than 0.1 m), and the power provided by the energy harvester can reach the maximum value through optimization so as to meet the use requirement. The invention provides a genetic algorithm-based high-power electromagnetic energy harvester structure optimization method, and further aims at key parameters (such as large stator outer diameter b) 2 And height h 2 Small stator outer diameter b 1 And height h 1 Small stator spacing d, suspension magnet height h 3 And coil spacing x, coil turns N and coil thickness L), encoding the same by using a genetic algorithm, converting an optimized variable into a gene on an individual, and screening a more appropriate individual by using the output power of the energy harvester as the fitness of the evaluated individual. The higher the fitness is, the stronger the competitiveness is, the easier the inheritance can be performed as a parent, and the optimal parameter solution can be obtained by repeating multiple rounds of inheritance.
The invention specifically adopts the following technical scheme:
a high-power electromagnetic energy harvester structure optimization method based on a genetic algorithm comprises the following steps:
s1, acquiring a parameter set to be optimized of an electromagnetic energy harvester and constraint conditions of the structure and the performance of the electromagnetic energy harvester, wherein each structure parameter in the parameter set to be optimized is preset with an optimized range;
s2, randomly sampling a plurality of groups of initial values of all structural parameters in the parameter set to be optimized in respective optimizable ranges, compiling the initial values of all the structural parameters of each group into a population individual, and enabling all the population individuals to form an initial parameter population;
s3, performing frequency sweep calculation on each population individual based on a structural parameter combination corresponding to each population individual in the initial parameter population, solving a suspension magnet motion equation of the electromagnetic energy harvester under the action of external excitation on each excitation frequency to obtain a calculated time domain response steady-state amplitude, obtaining a frequency domain response curve of the population individual by combining all frequencies to further obtain an output power target function value corresponding to each population individual, and then setting the fitness of the population individuals which do not meet the constraint condition in the initial parameter population to zero, and setting the fitness of the population individuals which meet the constraint condition as the output power target function value corresponding to the population individual;
s4, screening the initial parameter population according to the fitness of the current latest initial parameter population to establish a genetic parameter population; pairing the genetic parameter population to generate a genetic pairing parameter population; crossing the genetic pairing parameter population to obtain a genetic crossing parameter population; carrying out variation on the genetic cross parameter population to obtain a secondary genetic parameter population; based on the structural parameter combination corresponding to each population individual in the secondary genetic parameter population, obtaining an output power objective function value corresponding to each population individual by solving a suspension magnet motion equation of the electromagnetic energy harvester under the external excitation action, and then setting the fitness of the population individuals not meeting the constraint condition in the secondary genetic parameter population to zero, wherein the fitness of the population individuals meeting the constraint condition is set as the output power objective function value corresponding to the population individual; finally, taking the genetic parameter population as an initial parameter population of the next iteration;
and S5, continuously and iteratively executing the step S4 until a convergence condition of the genetic algorithm is reached, and selecting an optimal parameter solution from the parameter population so as to achieve an optimization target of enabling the output power of the energy harvester to be maximum.
Preferably, the electromagnetic energy harvester comprises a fixed magnet, a suspension magnet and a coil, wherein the fixed magnet is divided into a top magnet and a bottom magnet which have the same structural parameters and are composed of a large stator and a small stator, the two fixed magnets and the suspension magnet are arranged in a mutually exclusive mode, and the suspension magnet is suspended between the upper small stator and the lower small stator; the number of the coils is also two, and the upper coil and the lower coil respectively coaxially surround the outer parts of the upper stator and the lower stator.
Preferably, the structural parameters in the parameter set to be optimized of the electromagnetic energy harvester include the outer diameter and height of the large stator, the outer diameter and height of the small stator, the distance between the upper small stator and the lower small stator, the height of the suspension magnet, the distance between the upper coil and the lower coil, the number of turns of a single coil, and the height of the single coil.
Preferably, the constraints of the structure and performance of the electromagnetic energy harvester are as follows: the resonance frequency of the electromagnetic energy harvester is in the range of 150-170 Hz, and the total height is less than 0.1m.
Preferably, the motion equation of the levitation magnet under the external excitation action of the electromagnetic energy harvester is as follows:
under the condition of closed circuit and external excitation, the suspension magnet is subjected to mechanical damping force F d And the total magnetic damping force F of the induced currents in all the coils acting on the levitated magnet e Gravity F g Magnetic spring force F of stationary magnet acting on levitated magnet m The equation of motion is:
in the formula:
the relative acceleration of the levitation magnet is represented, m represents the mass of the levitation magnet, A represents the acceleration amplitude of an external excitation source, and omega represents the excitation frequency;
magnetic spring force F m The calculation formula of (c) is:
in the formula: z represents the relative position of the levitating magnet with respect to the center of the energy harvester, β 1 Denotes the residual magnetic flux of the levitating magnet, V denotes the volume of the levitating magnet, μ 0 Denotes the permeability of free space, B denotes the total magnetic field, θ z Represents the partial derivative of the relative position z of the levitated magnet;
mechanical damping force F d Total magnetic damping force F e The calculation formula of (2) is as follows:
in the formula: c represents a mechanical damping coefficient, and C (z) represents a functional relation between an equivalent magnetic damping coefficient and the displacement of the suspension magnet; n represents the number of coil turns, beta 1 Denotes the residual magnetic flux of the levitating magnet, V denotes the volume of the levitating magnet,
denotes the relative velocity of the levitating magnet, L denotes the coil thickness, z denotes the relative position of the levitating magnet, ζ denotes the distance of the top and bottom coils to the middle position of the energy harvester, D denotes the lineThe diameter of the loop.
Preferably, the method for obtaining the output power objective function value corresponding to each population individual by solving the motion equation of the suspension magnet of the electromagnetic energy harvester under the action of external excitation is as follows:
step-by-step numerical simulation is carried out on the motion process of the suspension magnet of the electromagnetic energy harvester under the action of external excitation, and the relative acceleration of the suspension magnet in the action of the external excitation is obtained by solving the motion equation in each simulation step
Relative velocity
And the relative position z, further obtaining an amplitude-frequency response curve of the electromagnetic energy harvester, and meanwhile, obtaining the amplitude-frequency response curve according to the relative speed
And calculating the voltages of the upper and lower coils at the relative position z:
calculating the total voltage epsilon generated by the coil according to the voltage difference between the upper and the lower coils 0 ＝ε 1 -ε 2 And calculating an output power objective function value of the electromagnetic energy harvester:
in the formula：ε c Representing the voltage across the load resistance, R 1 Representing the load resistance, R C The resistance of the coil consisting of the two coils is indicated, P the power generated by the load resistance.
Preferably, a fourth-order Runge-Kutta algorithm is adopted for solving the motion equation.
Preferably, the convergence condition of the genetic algorithm is that the population number of individuals in the parameter population tends to be stable.
Compared with the prior art, the invention has the following advantages: the invention adopts a genetic algorithm to optimize the structure of the high-power electromagnetic energy harvester, thereby greatly improving the energy harvesting power. In the optimization process, whether the constraint condition that the resonance frequency is in the range of more than 150Hz and less than 170Hz and the total height is less than 0.1m is met or not can be judged through electromechanical response, the optimal output power is achieved, and therefore the structural scheme of the energy harvester meeting the actual use requirement is obtained.
Drawings
FIG. 1 is a schematic diagram of the structure and key parameters of an electromagnetic energy harvester for use in the present invention;
FIG. 2 is a diagram of the steps of the energy harvester parameter optimization method based on the genetic algorithm of the present invention;
FIG. 3 is an iteration diagram of the genetic algorithm of the present invention;
FIG. 4 is a graph comparing magnetic spring forces experienced by an energy harvester of the present invention before and after optimization;
FIG. 5 is a graph of output power produced by the energy harvester of the present invention before and after optimization.
In the figure, 1-top large stator magnet, 2-top small stator magnet, 3-coil, 4-load resistor, 5-suspension magnet, 6-bottom small stator magnet and 7-bottom large stator magnet.
Detailed Description
Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.
As used herein, the singular forms "a", "an", "the" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises" and/or "comprising," when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
It will be understood by those skilled in the art that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the prior art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
Fig. 1 is a block diagram of a typical high power electromagnetic energy harvester that needs to be optimized for the present invention, and the main components of the electromagnetic energy harvester are a magnetic spring, i.e. a three magnet arrangement, an energy harvester housing, and a coil for power extraction. The electromagnetic energy harvester comprises fixed magnets, suspension magnets and coils, wherein the fixed magnets are divided into top magnets and bottom magnets with the same structural parameters, and are composed of a large stator and a small stator; the number of the coils is also two, and the upper coil and the lower coil respectively coaxially surround the outer parts of the upper stator and the lower stator. Wherein, the stator is fixed magnet, and the suspension magnet is the active cell. In this embodiment, the magnetic spring is composed of two fixed cylindrical magnets, i.e., an upper cylindrical magnet and a lower cylindrical magnet, and one solid levitation magnet. The upper cylindrical magnet comprises a top large stator magnet 1 and a top small stator magnet 2, and the lower cylindrical magnet comprises a bottom small stator magnet 6 and a bottom large stator magnet 7. In an initial state without external excitation, the levitation magnet 5 is disposed at a middle position of the two coils 3. Under external excitation, the levitating magnet 5 moves vertically within the housing of the electromagnetic energy harvester, and its kinetic energy is converted into useful electrical energy by the coil 3. The upper and lower coils are connected in series and are connected to an external load resistor 4.
The structural parameters to be optimized of the electromagnetic energy harvester and the corresponding constraint conditions can be selected according to the actual optimization purpose. In this embodiment, the constraint conditions for setting the structure and performance of the electromagnetic energy harvester are as follows: the resonance frequency of the electromagnetic energy harvester is in the range of 150-170 Hz, and the total height of the electromagnetic energy harvester is less than 0.1m. Due to the resonance frequency and magnetic spring force F of the energy harvester m In this regard, the present embodiment further selects a total of 9 critical parameters, large stator outer diameter b 2 And height h 2 Small stator outer diameter b 1 And height h 1 The distance d between the upper small stator and the lower small stator and the height h of the suspension magnet 3 And the distance x between the upper coil and the lower coil, the number of turns N of the single coil and the thickness L of the single coil. The 9 critical structural parameters to be optimized are also indicated in fig. 1.
The genetic algorithm is a random search algorithm by using natural selection and natural genetic mechanism in the biology world for reference. Compared with the traditional algorithm, the genetic algorithm is different from the traditional algorithm, does not depend on gradient information of the problem, and can reduce the risk of falling into the local optimal solution. The invention optimizes the structure of the high-power electromagnetic energy harvester through a genetic algorithm to obtain an optimal parameter solution with high efficiency. The energy harvester can generate larger power when the excitation frequency is close to the resonance frequency of the energy harvester. Therefore, genetic algorithm is used for carrying out gene coding on the 9 key structure parameters to be optimized, electro-magnetic-solid dynamic response simulation is carried out, constraint conditions that the resonance frequency of the energy harvester falls within the excitation frequency range and the total height of the energy harvester is not more than a certain size are realized by a method of endowing fitness, and a structural scheme of the energy harvester for capturing higher power is obtained after multi-generation iteration.
Based on the structure of the electromagnetic energy harvester shown in fig. 1, in order to realize parameter optimization, a corresponding numerical simulation model needs to be established to calculate the performance of the electromagnetic energy harvester under different structure parameters. Therefore, an analytical model of the nonlinear magnetic force and the magnetic damping force can be established firstly, then the formula of the magnetic force and the magnetic damping force is directly applied to the motion equation of the electromagnetic energy harvester, numerical simulation is carried out by solving the motion equation, and a basis is provided for optimization of structural parameters.
In addition, before the performance optimization calculation of the electromagnetic energy harvester is carried out by using a genetic algorithm, a proper gene coding rule and a fitness function need to be designed according to the characteristics of the output power optimization problem, the generation algorithm of the initial population is determined to improve the quality of the initial population, and a production voltage target function model of the electromagnetic energy harvester needed to be used in the calculation is determined.
The analytical formula of magnetic force and magnetic damping deduced in the invention establishes the motion equation of the energy harvester system. The motion equation of the suspension magnet of the electromagnetic energy harvester under the action of external excitation is as follows:
through analysis, the suspension magnet is subjected to mechanical damping force F under the conditions of closed circuit and external excitation d The total magnetic damping force F of the current induced in all the coils acting on the levitating magnet e Gravity F g Magnetic spring force F of stationary magnet acting on levitated magnet m Therefore, the equation of motion of the levitation magnet can be expressed as:
in the formula:
the relative acceleration of the levitation magnet is shown, m represents the mass of the levitation magnet, a represents the acceleration amplitude of the excitation source, and ω represents the excitation frequency.
Wherein the critical magnetic spring force F m The calculation formula of (c) is:
in the formula: z represents the levitation magnet relative to the energy harvesterRelative position of the heart, beta 1 Denotes the residual magnetic flux of the levitating magnet, V denotes the volume of the levitating magnet, μ 0 Denotes the permeability of free space, B denotes the total magnetic field, theta z Represents the partial derivative of the relative position z of the levitated magnet;
in addition, gravity F in the equation of motion g Of known quantity, and a mechanical damping force F d Total magnetic damping force F e It can be calculated by the following formula:
in the formula: c represents the mechanical damping coefficient, and C (z) represents the functional relation between the equivalent magnetic damping coefficient and the displacement of the suspension magnet; n represents the number of coil turns, beta 1 Denotes the residual magnetic flux of the levitation magnet, V denotes the volume of the levitation magnet,
denotes the relative velocity of the levitating magnet, L denotes the coil thickness, z denotes the relative position of the levitating magnet, ζ denotes the distance of the top and bottom coils to the middle position of the energy harvester, and D denotes the diameter of the coils.
Based on the suspension magnet motion equation constructed above, under the condition of known external excitation, the output power objective function value under the structural parameter corresponding to each population individual can be obtained by solving the suspension magnet motion equation of the electromagnetic energy harvester under the external excitation action in the genetic algorithm, and the specific method is as follows:
carrying out frequency domain and time domain numerical simulation on the motion process of the suspension magnet of the electromagnetic energy harvester under the action of external excitation, and carrying out numerical simulation on each excitationFrequency is simulated by solving the time domain equation of motion to obtain the relative acceleration of the suspension magnet in the external excitation action process
Relative speed
And the relative position z, so that an amplitude-frequency response curve of the electromagnetic energy harvester is obtained. Meanwhile, the output voltage generated by the electromagnetic energy harvester and the resistance in the coil can be calculated according to the structural parameter information in the population individual, and when the internal resistance of the coil is the same as the load resistance value, the output power reaches the maximum, so that the output power can be obtained by calculating the voltage and the resistance in the coil. Specifically, first, the velocity is measured
And calculating the voltages of the upper and lower coils at the relative position z:
because the upper and lower coils are connected with each other and the winding directions are opposite, the total voltage epsilon generated by the coils can be calculated according to the voltage difference between the upper and lower coils 0 ＝ε 1 -ε 2 And calculating the output power objective function value of the electromagnetic energy harvester:
in the formula：ε c Representing the voltage across the load resistance, R 1 Representing the load resistance, R C Denotes the resistance of the coil consisting of the two coils, P denotes the power generated by the load resistance.
Therefore, the motion equation can be used, the power objective function value can be solved through the motion equation, the solving method can adopt any feasible method in the prior art, and the numerical solution can be carried out on the established motion equation of the energy harvester system by using a Runge-Kutta method in the embodiment. In this embodiment, the fitness of the population individual may be directly replaced with the power objective function value, but it needs to be considered whether the population individual satisfies the constraint condition.
Referring to fig. 2, which is a flow chart of the genetic algorithm-based energy harvester parameter optimization method of the present invention, specific steps of the optimization method are described in detail below with reference to the soil, and the steps include S1 to S5.
S1, acquiring a parameter set to be optimized of an electromagnetic energy harvester and constraint conditions of the structure and the performance of the electromagnetic energy harvester, wherein each structure parameter in the parameter set to be optimized is preset with an optimized range. Subsequent genetic algorithm optimization requires parameter optimization within the respective optimizable ranges of these structural parameters.
S2, randomly sampling a plurality of groups of initial values of all structural parameters in the parameter set to be optimized in respective optimizable ranges, compiling the initial values of all the structural parameters in each group into a population individual through a coding rule, and enabling all the population individuals to form an initial parameter population.
Specifically, the gene coding rule used in this example is designed as follows:
and constructing individuals representing the performance of the energy harvester according to the key structure parameters of the electromagnetic energy harvester so as to determine the encoding rule. The control variable for the performance of the electromagnetic energy harvester in this embodiment comprises the outer diameter b of the large stator 2 And height h 2 Small stator outer diameter b 1 And height h 1 The distance d between the upper small stator and the lower small stator and the height h of the suspension magnet 3 And 9 relations of the distance x between the upper coil and the lower coil, the number of turns N of the single coil and the thickness L of the single coilA key parameter. A group of values of the 9 key structure parameters of the electromagnetic energy harvester forms a group individual, and the parameter information in the group individual comprises a combination of 9 structure parameter values, which is subsequently called as a structure parameter combination. The genetic algorithm optimizes the dyeing individuals continuously according to a genetic strategy in the population evolution process, namely simultaneously optimizes the structural parameters, thereby achieving the purpose of simultaneously optimizing the electromagnetic energy harvester. The encoding mode can adopt binary encoding, and an encoded individual is represented as follows:
X＝[X 0 …X 9 …X 18 X 19 …X 28 X 29 …X 89 ]
in which each 10 components represents a structural parameter, e.g. X 0 …X 9 Large stator outer diameter b representing the first structural parameter 2 ，X 10 …X 19 Representing the second constructional parameter, large stator height h 2 . And so on.
S3, performing frequency sweep calculation on each population based on the structural parameter combination corresponding to each population in the initial parameter population, solving a motion equation of the suspension magnet of the electromagnetic energy harvester under the action of external excitation on each excitation frequency to obtain a calculated time domain response steady-state amplitude, combining all frequencies to obtain a frequency domain response curve of each population, further obtaining an output power target function value corresponding to each population, and then setting the fitness of the population not meeting the constraint condition in the initial parameter population to zero, and setting the fitness of the population meeting the constraint condition as the output power target function value corresponding to the population.
S4, screening the initial parameter population according to the fitness of the current latest initial parameter population to establish a genetic parameter population; pairing the genetic parameter population to generate a genetic pairing parameter population; crossing the genetic pairing parameter population to obtain a genetic crossing parameter population; carrying out variation on the genetic cross parameter population to obtain a secondary genetic parameter population; based on the structural parameter combination corresponding to each population individual in the secondary genetic parameter population, solving a suspension magnet motion equation of the electromagnetic energy harvester under the action of external excitation by a Runge-Kutta method to obtain an output power target function value corresponding to each population individual, and then setting the fitness of the population individuals not meeting the constraint condition in the secondary genetic parameter population to zero, wherein the fitness of the population individuals meeting the constraint condition is set as the output power target function value corresponding to the population individual; and finally, taking the genetic parameter population as an initial parameter population of the next iteration.
And S5, continuously and iteratively executing the step S4 until a convergence condition of the genetic algorithm is reached, and selecting an optimal parameter solution from the parameter population so as to achieve an optimization target of maximizing the output power of the energy harvester.
The convergence condition of the genetic algorithm may be selected according to practice. In this embodiment, the convergence condition of the genetic algorithm is that the population number of individuals in the parameter population tends to be stable. When the number of individuals in the parameter population tends to be stable, the parameter population is basically the same optimal solution, so that the optimal parameter solution can be selected to achieve the optimization target, namely the maximum output power of the energy harvester.
The results of the above optimization method applied in this example are shown in fig. 3 to 5:
as shown in fig. 3, with continuous iteration of the population with the initial parameters, individuals with high fitness are kept in the population, and the average output power of the individuals in the population is also continuously increased. Because the individual with the maximum output power objective function value is set as a negative value, the individual with the minimum negative value of the output power objective function value is obtained through multiple iterations, and the obtained individual is the optimal parameter solution. Therefore, the output power target function value is reduced in a curve along with population iteration, and when the curve tends to be horizontal, the output power reaches the maximum.
As shown in fig. 4, the magnetic spring force applied to the levitating magnet of the electromagnetic energy harvester during a small displacement range changes substantially linearly, so that the resonant frequency of the electromagnetic energy harvester can be calculated.
As shown in fig. 5, the output power produced by the electromagnetic energy harvester varies with the excitation frequency. The optimized electromagnetic energy harvester generates the maximum voltage when external excitation is about 151Hz, and the resonance frequency of the electromagnetic energy harvester is 151Hz.
This indicates that: the invention relates to a genetic algorithm-based high-power electromagnetic energy harvester structure optimization method, which optimizes the influence of parameters such as the outer diameter and height of large and small stators, the distance between small stators, the height of a suspension magnet, the distance between coils, the number of turns of the coils, the thickness of the coils and the like on the energy harvesting power of an energy harvester by means of a genetic algorithm, so that the energy harvester can be used under the limiting conditions: the resonance frequency is more than 150Hz and less than 170Hz, and the total height is less than 0.1m, to reach the optimized output power. And the actual use requirement is met.
The present invention is not the best known technology, for example, the processes of population pairing, crossing, mutation, etc. in the genetic algorithm can be referred to the known principle of the genetic algorithm itself, and can also be directly realized by adopting the related programs packaged in the prior art, and the description is not made.
The above embodiments are merely illustrative of the technical ideas and features of the present invention, and the purpose thereof is to enable those skilled in the art to understand the contents of the present invention and implement the present invention, and not to limit the protection scope of the present invention. All equivalent changes and modifications made according to the spirit of the present invention should be covered in the protection scope of the present invention.
Claims (8)
1. A high-power electromagnetic energy harvester structure optimization method based on a genetic algorithm is characterized by comprising the following steps:
s1, acquiring a parameter set to be optimized of an electromagnetic energy harvester and constraint conditions of the structure and the performance of the electromagnetic energy harvester, wherein each structure parameter in the parameter set to be optimized is preset with an optimized range;
s2, randomly sampling a plurality of groups of initial values of all structural parameters in a parameter set to be optimized in respective optimizable ranges, compiling the initial values of all the structural parameters of each group into a population individual, and enabling all the population individuals to form an initial parameter population;
s3, performing frequency sweep calculation on each population individual based on a structural parameter combination corresponding to each population individual in the initial parameter population, solving a suspension magnet motion equation of the electromagnetic energy harvester under the action of external excitation on each excitation frequency to obtain a calculated time domain response steady-state amplitude, obtaining a frequency domain response curve of the population individual by combining all frequencies to further obtain an output power target function value corresponding to each population individual, and then setting the fitness of the population individuals which do not meet the constraint condition in the initial parameter population to zero, and setting the fitness of the population individuals which meet the constraint condition as the output power target function value corresponding to the population individual;
s4, screening the initial parameter population according to the fitness of the current latest initial parameter population to establish a genetic parameter population; pairing the genetic parameter population to generate a genetic pairing parameter population; crossing the genetic pairing parameter population to obtain a genetic crossing parameter population; carrying out variation on the genetic cross parameter population to obtain a secondary genetic parameter population; based on the structural parameter combination corresponding to each population individual in the secondary genetic parameter population, obtaining an output power objective function value corresponding to each population individual by solving a suspension magnet motion equation of the electromagnetic energy harvester under the external excitation action, and then setting the fitness of the population individuals not meeting the constraint condition in the secondary genetic parameter population to zero, wherein the fitness of the population individuals meeting the constraint condition is set as the output power objective function value corresponding to the population individual; finally, taking the genetic parameter population as an initial parameter population of the next iteration;
and S5, continuously and iteratively executing the step S4 until a convergence condition of the genetic algorithm is reached, and selecting an optimal parameter solution from the parameter population so as to achieve an optimization target of enabling the output power of the energy harvester to be maximum.
2. The genetic algorithm-based structural optimization method for the high-power electromagnetic energy harvester, as set forth in claim 1, wherein the electromagnetic energy harvester comprises fixed magnets, floating magnets and coils, wherein the fixed magnets are divided into top magnets and bottom magnets with the same structural parameters, and are composed of large stators and small stators, two fixed magnets and floating magnets are arranged in a mutually exclusive manner, and the floating magnets are suspended between the upper and lower small stators; the number of the coils is two, and the upper coil and the lower coil coaxially surround the outer parts of the upper stator and the lower stator respectively.
3. The method for optimizing the structure of the high-power electromagnetic energy harvester based on the genetic algorithm of claim 1, wherein the structural parameters in the parameter set to be optimized of the electromagnetic energy harvester comprise the outer diameter and height of a large stator, the outer diameter and height of a small stator, the distance between an upper small stator and a lower small stator, the height of a suspension magnet, the distance between an upper coil and a lower coil, the number of turns of a single coil and the height of a single coil.
4. The method for optimizing the structure of the high-power electromagnetic energy harvester based on the genetic algorithm of claim 1, wherein the constraints of the structure and the performance of the electromagnetic energy harvester are as follows: the resonance frequency of the electromagnetic energy harvester is in the range of 150-170 Hz, and the total height is less than 0.1m.
5. The method for optimizing the structure of the high-power electromagnetic energy harvester based on the genetic algorithm as claimed in claim 1, wherein the equations of motion of the levitation magnets of the electromagnetic energy harvester under the action of external excitation are as follows:
under the condition of closed circuit and external excitation, the suspension magnet is subjected to mechanical damping force F d And the total magnetic damping force F of the induced currents in all the coils acting on the levitated magnet e Gravity F g Magnetic spring force F of static magnet acting on suspension magnet m The equation of motion is:
in the formula:
representing the relative acceleration of the levitation magnet, m representing the mass of the levitation magnet, a representing the acceleration amplitude of the external excitation source, and ω representing the excitation frequency;
magnetic spring force F m The calculation formula of (2) is as follows:
in the formula: z represents the relative position of the levitating magnet with respect to the center of the energy harvester, β 1 Denotes the residual magnetic flux of the levitating magnet, V denotes the volume of the levitating magnet, μ 0 Denotes the permeability of free space, B denotes the total magnetic field,
represents the partial derivative of the relative position z of the levitated magnet;
mechanical damping force F d Total magnetic damping force F e The calculation formula of (c) is:
in the formula: c represents a mechanical damping coefficient, and C (z) represents a functional relation between an equivalent magnetic damping coefficient and the displacement of the suspension magnet; n denotes the number of turns of the coil, beta 1 Denotes the residual magnetic flux of the levitating magnet, V denotes the volume of the levitating magnet,
denotes the relative velocity of the levitating magnet, L denotes the thickness of the coil, z denotes the relative position of the levitating magnet, ζ denotes the distance of the top and bottom coils to the middle position of the energy harvester, and D denotes the diameter of the coil.
6. The method for optimizing the structure of the high-power electromagnetic energy harvester based on the genetic algorithm as claimed in claim 1, wherein the method for obtaining the output power objective function value corresponding to each population by solving the motion equation of the suspension magnet of the electromagnetic energy harvester under the external excitation action comprises the following steps:
step-by-step numerical simulation is carried out on the motion process of the suspension magnet of the electromagnetic energy harvester under the action of external excitation, and the relative acceleration of the suspension magnet in the action of the external excitation is obtained by solving the motion equation in each simulation step
Relative velocity
And the relative position z, further obtaining an amplitude-frequency response curve of the electromagnetic energy harvester, and meanwhile, obtaining the amplitude-frequency response curve according to the relative speed
And calculating the voltages of the upper and lower coils at the relative position z:
calculating the total voltage epsilon generated by the coil according to the voltage difference between the upper and lower coils 0 ＝ε 1 -ε 2 And calculating the output power objective function value of the electromagnetic energy harvester:
in the formula: epsilon c Representing the voltage across the load resistance, R 1 Representing the load resistance, R C The resistance of the coil consisting of the two coils is indicated, P the power generated by the load resistance.
7. The method for optimizing the structure of the high-power electromagnetic energy harvester based on the genetic algorithm as claimed in claim 1, wherein the equation of motion is solved by a fourth-order Runge-Kutta algorithm.
8. The method for optimizing the structure of the high-power electromagnetic energy harvester based on the genetic algorithm as claimed in claim 1, wherein the convergence condition of the genetic algorithm is that the population number of the parameter population tends to be stable.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211506528.7A CN115795860A (en) | 2022-11-28 | 2022-11-28 | Genetic algorithm-based high-power electromagnetic energy harvester structure optimization method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211506528.7A CN115795860A (en) | 2022-11-28 | 2022-11-28 | Genetic algorithm-based high-power electromagnetic energy harvester structure optimization method |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN115795860A true CN115795860A (en) | 2023-03-14 |
Family
ID=85442641
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202211506528.7A Pending CN115795860A (en) | 2022-11-28 | 2022-11-28 | Genetic algorithm-based high-power electromagnetic energy harvester structure optimization method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115795860A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117350135A (en) * | 2023-12-04 | 2024-01-05 | 华东交通大学 | Frequency band expanding method and system of hybrid energy collector |
-
2022
- 2022-11-28 CN CN202211506528.7A patent/CN115795860A/en active Pending
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117350135A (en) * | 2023-12-04 | 2024-01-05 | 华东交通大学 | Frequency band expanding method and system of hybrid energy collector |
| CN117350135B (en) * | 2023-12-04 | 2024-03-08 | 华东交通大学 | Frequency band expanding method and system of hybrid energy collector |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Tang et al. | Design and optimization of a tubular linear electromagnetic vibration energy harvester | |
| CN102035432B (en) | Multidirectional vibration energy recovery structure | |
| CN107563001B (en) | Vehicle ISD suspension electromechanical network system real integration and passive realization method | |
| CN111030316B (en) | Modeling, analysis and system principle analysis method for efficiency of multi-relay MC-WPT system | |
| Drezet et al. | Design of a nonlinear energy harvester based on high static low dynamic stiffness for low frequency random vibrations | |
| CN115795860A (en) | Genetic algorithm-based high-power electromagnetic energy harvester structure optimization method | |
| CN105680720A (en) | Multi-degree-of-freedom piezoelectric-electromagnetic composite multi-directional broadband kinetic energy collector | |
| Yang et al. | A new hybrid piezoelectric-electromagnetic vibration-powered generator and its model and experiment research | |
| Constantinou et al. | A model of a magnetically sprung vibration generator for power harvesting applications | |
| CN206481204U (en) | A kind of double freedom magnetic suspension type vibration energy collecting device | |
| Wang et al. | Resonant frequency reduction of vertical vibration energy harvester by using negative-stiffness magnetic spring | |
| Cottone et al. | Electromagnetic buckled beam oscillator for enhanced vibration energy harvesting | |
| CN103762889B (en) | Based on the lever vibrational energy collector of giant magnetostrictive thin film | |
| Rubes et al. | Development of vibration energy harvester fabricated by rapid prototyping technology | |
| CN106602835A (en) | Two-degree-of-freedom magnetic levitation vibration energy harvester and manufacturing method thereof | |
| KR101361576B1 (en) | Multi-frequency electromagnetic energy harvester | |
| Toluwaloju et al. | Investigation on the Effect of Magnetic Coupling and Power Harvested in an Electromagnetic Vibration Energy Harvester | |
| Noras et al. | Optimization of electromagnetic and vibration energy harvesters utilizing flexible piezoelectric cantilever beams | |
| Toluwaloju et al. | A Parametric Analysis on Performance Dependence of Electromagnetic Vibration Harvester on the Coil Position, Coil Connection, and Magnetic Flux Density | |
| Ye et al. | Analysis of multi-direction low-frequency vibration energy harvester using diamagnetic levitation | |
| Wang et al. | A microelectroplated magnetic vibration energy scavenger for wireless sensor microsystems | |
| Mullen | Design optimization of an electromagnetic energy harvester backpack for utilization of human walking energy | |
| Suriyatni et al. | Analysis Of Wireless Energy Transfer Systems By Methods Inductive Resonance | |
| CN203416191U (en) | Array type composite energy collection device | |
| Tang et al. | Electromagnetic vibration energy harvesting with high power density using a magnet array |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |